This study addresses the application of encoder-only Pre-trained Language
Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of
domain-tailored encoder-only models compared to encoder-decoder models. We
investigate three core inquiries: (1) the efficacy of encoder-only PLMs in KPG,
(2) optimal architectural decisions for employing encoder-only PLMs in KPG, and
(3) a performance comparison between in-domain encoder-only and encoder-decoder
PLMs across varied resource settings. Our findings, derived from extensive
experimentation in two domains reveal that with encoder-only PLMs, although KPE
with Conditional Random Fields slightly excels in identifying present
keyphrases, the KPG formulation renders a broader spectrum of keyphrase
predictions. Additionally, prefix-LM fine-tuning of encoder-only PLMs emerges
as a strong and data-efficient strategy for KPG, outperforming general-domain
seq2seq PLMs. We also identify a favorable parameter allocation towards model
depth rather than width when employing encoder-decoder architectures
initialized with encoder-only PLMs. The study sheds light on the potential of
utilizing encoder-only PLMs for advancing KPG systems and provides a groundwork
for future KPG methods. Our code and pre-trained checkpoints are released at
this https URL

该研究探讨了仅具有编码器的预训练语言模型在关键词生成中的应用，研究了优化架构决策以及对资源设置进行性能比较的效果。研究发现，使用仅具有编码器的预训练语言模型进行关键词生成能够广泛预测关键词，而使用前缀语言模型对编码器进行微调则是一种强大且高效的策略。该研究对推动关键词生成系统的发展提供了有价值的见解，并为未来的研究方法奠定了基础。